In many circumstances, the segmentation of a mass candidate is based on an initial identification of a kernel area. However, identification of the initial kernel is often a non-robust process, that is, the exact definition of this kernel may be influenced by irrelevant factors. As a consequence, a non-robust segmentation of the mass candidate may be obtained, as can be illustrated by two examples.
In a Computer Aided Diagnosis (CAD) process, many mass candidates are usually generated. In order for the CAD process to attain a high sensitivity and specificity, the probability of malignancy for each mass candidate needs to be evaluated precisely. This is achieved using a large number of quantitative features that are extracted from the area of the candidate in the image. For ensuring the highest possible statistical significance of the extracted features, an accurate definition of the mass candidate area, that is mass segmentation, is needed.
The CAD mass candidate is often initially identified by some kernel area or by a rectangle or other closed contour approximately enclosing the candidate. The exact position of this identifier, i.e. the kernel or the enclosing contour, is influenced, inter alia by the positioning of the film mammogram in the feeder as well as by small variations in the brightness and contrast of the scanned image. It is also influenced by imaging conditions (KeV, mAs). In order for the overall CAD process to be robust, the mass candidate segmentation should not be influenced by such slight and insignificant variations in the image.
As an additional example of a non-robust segmentation of mass candidates, the following can be considered. A viewing station used for soft reading of mammography images may provide additional information on objects that are mass candidates selected by the radiologist. In such a context, the radiologist needs to select the object he wants to consider using a pointing device. This can be done, for example, by clicking within the object or by drawing a rectangle or any other circumscribing contour around the object. The naturally high variability of such object definition by the user produces a lack of robustness in the segmented object and, consequently, a lack of reliability in any additional information computed for the object.
The way the mass candidate is segmented depends greatly on what is seen as the ‘center’ of the mass. For example, a contour of the object may be constructed by analyzing the profile of grey levels using a gradient analysis along lines radiating from the ‘center’. If the object's ‘center’ is moved, different radiating lines will be analyzed and a different contour will be obtained.
Due to the complexity of the internal structure of a mass candidate and to the large variability between candidates, it is at present difficult to define in an unequivocal manner an analytical method for the determination of the ‘center’ of the object.